cloud-infrastructureai-ml

Novita AI GPU Cloud▌

by novitalabs

Access Novita AI GPU cloud for cloud-based GPU computing. Deploy, monitor, and manage GPU workloads and containers with

Provides direct access to Novita AI's GPU cloud infrastructure for deploying, monitoring, and managing GPU-accelerated workloads and containers without leaving your conversation context.

github stars

★ 12

GitHub npm

Direct GPU cloud access from chat interfaceBeta release with active development

best for

/ AI/ML developers needing GPU compute resources
/ Teams deploying containerized GPU workloads
/ Researchers running compute-intensive experiments

capabilities

/ Deploy GPU instances with custom configurations
/ Monitor and manage running GPU containers
/ Create and manage container templates
/ Configure network storage for GPU workloads
/ Control instance lifecycle (start, stop, restart, delete)
/ Manage container registry authentication

what it does

Connects to Novita AI's GPU cloud platform to deploy, monitor, and manage GPU-accelerated containers and workloads. Currently in beta with GPU instance management capabilities.

about

Novita AI GPU Cloud is an official MCP server published by novitalabs that provides AI assistants with tools and capabilities via the Model Context Protocol. Access Novita AI GPU cloud for cloud-based GPU computing. Deploy, monitor, and manage GPU workloads and containers with It is categorized under cloud infrastructure, ai ml.

how to install

You can install Novita AI GPU Cloud in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Novita AI GPU Cloud is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Novita MCP Server

novita-mcp-server is a Model Context Protocol (MCP) server that provides seamless interaction with Novita AI platform resources. We recommend accessing this server through Claude Desktop, Cursor, or any other compatible MCP client.

Features

⚠️ Beta Notice: novita-mcp-server is currently in beta and only supports GPU instance management. Additional resource types will be supported in future releases.

Currently, novita-mcp-server enables management the resources of GPU instances product.

Supported operations are as follows:

Cluster(/Region): List;
Product: List;
GPU Instance: List, Get, Create, Start, Stop, Delete, Restart;
Template: List, Get, Create, Delete;
Container Registry Auth: List, Create, Delete;
Network Storage: List, Create, Update, Delete;

Installation

You can install the package using npm, or Smithery:

Using npm

npm install -g @novitalabs/novita-mcp-server

Using Smithery

Visit the https://smithery.ai/server/@novitalabs/novita-mcp-server and follow the "Install" instructions to install the server.

Configuration to use novita-mcp-server

First, you need to get your Novita API key from the Novita AI Key Management.

And next, you can use the following configuration for both Claude Desktop and Cursor:

📌 Tips

For Claude Desktop, you can refer to the Claude Desktop MCP Quickstart guide to learn how to configure the MCP server.

For Cursor, you can refer to the Cursor MCP Quickstart guide to learn how to configure the MCP server.

{
  "mcpServers": {
    "@novitalabs/novita-mcp-server": {
      "command": "npx",
      "args": ["-y", "@novitalabs/novita-mcp-server"],
      "env": {
        "NOVITA_API_KEY": "your_api_key_here"
      }
    }
  }
}

Examples

Here are some examples of how to use the novita-mcp-server to manage your resources with Claude Desktop or Cursor:

List clusters

List all the Novita clusters

List products

List all available Novita GPU instance products

List GPU instances

List all my running Novita GPU instances

Create a new GPU instance

Create a new Novita GPU instance:

Name: test-novita-mcp-server-01
Product: any available product
GPU Number: 1
Image: A standard public PyTorch/CUDA image
Container Disk: 60GB

Testing

This project uses Jest for testing. The tests are located in the src/tests directory.

You can run the tests using one of the following commands:

npm test